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ORCIDDiana-Lucia Miholca
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2020 – today
- 2023
[c13]Diana-Lucia Miholca
, Zsuzsanna Onet-Marian:
On the Relevance of Graph2Vec Source Code Embeddings for Software Defect Prediction. ICSOFT (Selected Papers) 2023: 124-154- [c12]Diana-Lucia Miholca, Zsuzsanna Onet-Marian:
Source-Code Embedding-Based Software Defect Prediction. ICSOFT 2023: 185-196 - 2022
- [j4]Diana-Lucia Miholca, Vlad-Ioan Tomescu, Gabriela Czibula:
An in-Depth Analysis of the Software Features' Impact on the Performance of Deep Learning-Based Software Defect Predictors. IEEE Access 10: 64801-64818 (2022) - 2021
- [c11]Diana-Lucia Miholca:
New Conceptual Cohesion Metrics: Assessment for Software Defect Prediction. SYNASC 2021: 163-170 - 2020
- [c10]Diana-Lucia Miholca, Gabriela Czibula, Vlad-Ioan Tomescu:
COMET: A conceptual coupling based metrics suite for software defect prediction. KES 2020: 31-40 - [c9]Diana-Lucia Miholca, Zsuzsanna Onet-Marian:
An analysis of aggregated coupling's suitability for software defect prediction. SYNASC 2020: 141-148
2010 – 2019
- 2019
- [j3]Gabriela Czibula, István Gergely Czibula, Diana-Lucia Miholca, Liana Maria Crivei:
A novel concurrent relational association rule mining approach. Expert Syst. Appl. 125: 142-156 (2019) - [j2]István Gergely Czibula, Gabriela Czibula, Diana-Lucia Miholca, Zsuzsanna Marian:
An aggregated coupling measure for the analysis of object-oriented software systems. J. Syst. Softw. 148: 1-20 (2019) - [c8]Diana-Lucia Miholca, Gabriela Czibula:
DynGRAR: A dynamic approach to mining gradual relational association rules. KES 2019: 10-19 - [c7]Diana-Lucia Miholca, Gabriela Czibula:
Software Defect Prediction Using a Hybrid Model Based on Semantic Features Learned from the Source Code. KSEM (1) 2019: 262-274 - 2018
- [j1]Diana-Lucia Miholca, Gabriela Czibula, István Gergely Czibula:
A novel approach for software defect prediction through hybridizing gradual relational association rules with artificial neural networks. Inf. Sci. 441: 152-170 (2018) - [c6]Diana-Lucia Miholca, Gabriela Czibula, Liana Maria Crivei:
A new incremental relational association rules mining approach. KES 2018: 126-135 - [c5]Diana-Lucia Miholca:
An Improved Approach to Software Defect Prediction using a Hybrid Machine Learning Model. SYNASC 2018: 443-448 - 2017
- [c4]Diana-Lucia Miholca, Adrian Onicas:
Detecting depression from fMRI using relational association rules and artificial neural networks. ICCP 2017: 85-92 - [c3]Diana-Lucia Miholca, Gabriela Czibula, Zsuzsanna Marian, István Gergely Czibula:
An Unsupervised Learning Based Conceptual Coupling Measure. SYNASC 2017: 247-254 - 2016
- [c2]Diana-Lucia Miholca, Gabriela Czibula, Ioan-Gabriel Mircea, István Gergely Czibula:
Machine Learning Based Approaches for Sex Identification in Bioarchaeology. SYNASC 2016: 311-314 - 2015
- [c1]Vlad-Sebastian Ionescu, Ioan-Gabriel Mircea, Diana-Lucia Miholca, Gabriela Czibula:
Instance based learning approaches for predicting the height of human skeletons. ICCP 2015: 309-316
Coauthor Index
aka: Gabriela Czibula
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